SWE 249P
Applied Data Analytics
Exploration of the concepts, terminology, and processes used in supervised and unsupervised learning for classification and clustering, from a software engineering perspective. Topics include K-Nearest Neighbors, Logistic Regression, Random Forests, Support Vector Machines, K-Means, and model assessment.
Topics
- K-Nearest Neighbors
- Logistic Regression
- Random Forests
- Support Vector Machines
- K-Means
- and model assessment
Topics explicitly listed in official catalogue description
Prerequisites & course information
Exploration of the concepts, terminology, and processes used in supervised and unsupervised learning for classification and clustering, from a software engineering perspective. Topics include K-Nearest Neighbors, Logistic Regression, Random Forests, Support Vector Machines, K-Means, and model assessment.
Sources
- Course catalogue ↗2026-2027